A combined deep CNN with a chimp optimization approach for lung cancer diagnosis

Abstract

The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer.